AI-102 Implement computer vision solutions Practice Test 4 — 15 Questions

Question 1

Which THREE factors should be considered when choosing between Azure AI Vision prebuilt models and Custom Vision?

Accepted Answer

Prebuilt models may not have high accuracy for industry-specific objects.. Prebuilt models are trained on general datasets (e.g., ImageNet) and may not achieve high accuracy for industry-specific objects such as specialized medical instruments or unique manufacturing parts. Custom Vision allows fine-tuning on domain-specific images to improve precision for such niche categories.

Answer

Custom Vision cannot process images from real-time video feeds.

Answer

Prebuilt models require a large training dataset.

Question 2

Which TWO Azure AI services can perform optical character recognition (OCR)?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) includes a prebuilt read model that extracts printed and handwritten text from documents, making it a valid OCR service. The Read API, part of Azure AI Vision, is specifically designed for OCR and can extract text from images and PDFs. Both services provide optical character recognition capabilities, though they target different use cases: Document Intelligence focuses on structured document analysis, while Read API is a general-purpose OCR engine.

Answer

Custom Vision

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Face API

Answer

Video Indexer

Question 3

You are analyzing an image using the Azure AI Vision REST API with the JSON request above. The response includes a description: 'a person holding a smartphone'. However, the response does not include any brand information even though the smartphone is clearly visible. What is the most likely reason?

Accepted Answer

The smartphone brand is not in the Microsoft brand catalog.. The Azure AI Vision API's brand detection feature relies on a predefined Microsoft brand catalog. If the smartphone brand is not included in that catalog, the API will not return brand information even if the brand is clearly visible in the image. The catalog covers major brands but may not include all niche or regional smartphone manufacturers.

Answer

The 'brands' feature was not included in the request.

Answer

The 'model-version' is set to 'latest' which does not support brand detection.

Answer

The API only detects one brand per image, and a different brand was detected.

Question 4

You are developing a solution for a hospital that uses the Face API to identify patients from photos taken at check-in. The system must be HIPAA compliant. You need to ensure that face data is protected and not retained longer than necessary. The hospital wants to use the Face API for identification only during the patient's visit. After discharge, the face data should be deleted. What is the recommended approach?

Accepted Answer

Create a Person Group for each day, add patients, and delete the Person Group after the day ends.. Creating a Person Group per day allows you to logically group face data for that day's patients. After the day ends, deleting the entire Person Group removes all associated face data from the Face API service in a single operation, ensuring HIPAA compliance by not retaining data longer than necessary. This approach aligns with the Face API's lifecycle management, where Person Groups are the container for face IDs and their deletion cascades to all stored face data.

Answer

Use the Face API with a subscription key and export face data to local storage, then delete from the cloud.

Answer

Store the face IDs in a database and delete them manually after discharge.

Answer

Use the Face API Identify operation and then delete the face ID from the Person Group.

Question 5

A retail company uses the Computer Vision Image Analysis API to generate tags for product images in their e-commerce catalog. They want to automatically tag images with product categories such as 'electronics', 'clothing', and 'home goods'. The prebuilt tags often misclassify items. For example, a smartphone is tagged as 'communication device' instead of 'electronics'. You need to improve the tagging accuracy for the company's specific product categories without building a completely new model. What should you do?

Accepted Answer

Train a Custom Vision classification model with images labeled with the company's product categories.. Training a Custom Vision classification model with images labeled with the company's specific product categories is the best way to improve tagging accuracy without building a completely new model from scratch. Custom Vision allows you to use transfer learning to fine-tune a pre-trained model on your own dataset, enabling it to recognize your custom categories such as 'electronics', 'clothing', and 'home goods'. This approach leverages the existing model's feature extraction capabilities while adapting to your specific domain.

Answer

Use the Dense Captioning feature to generate detailed descriptions and parse them for categories.

Answer

Increase the confidence threshold for tags to reduce false positives.

Answer

Use the 'brands' feature to identify product brands and map them to categories.

Question 6

A manufacturing company uses Azure AI Custom Vision to classify defects on assembly line parts. The model is currently trained with 500 images per class and achieves 85% accuracy. The company needs to improve accuracy to 95% without collecting new images. Which THREE actions should they take?

Accepted Answer

Use data augmentation (e.g., rotation, scaling). Custom Vision's built-in data augmentation (rotation, scaling, flipping, etc.) artificially increases the diversity of the training set without requiring new images. This helps the model generalize better and reduces overfitting, directly improving accuracy toward the 95% target.

Answer

Change the domain to 'General'

Answer

Increase the number of training iterations

Question 7

You are a developer for a healthcare startup. They are building a mobile app that allows users to take photos of prescription labels and extract medication names, dosages, and frequencies. The app must run on iOS and Android devices. The solution should use a pre-built AI service with minimal custom code. What should you recommend?

Accepted Answer

Use Azure AI Vision OCR API to extract text from the label images. Azure AI Vision's OCR (Read) API is a prebuilt service that extracts printed and handwritten text from images with no training required, and it works cross-platform via REST/SDK on iOS and Android. For prescription labels containing printed text, this delivers the medication names, dosages, and frequencies with minimal custom code. It is the fastest path to a working mobile OCR feature.

Answer

Use Azure AI Document Intelligence (Form Recognizer) to analyze labels

Answer

Train a Custom Vision object detection model to locate and read text

Answer

Use Azure AI Language to extract entities from the label text

Question 8

A retail company uses Azure AI Vision to analyze in-store video feeds. They want to detect when employees are not wearing required safety vests. The solution must send real-time alerts to store managers. Video feeds are captured from existing CCTV cameras. Which approach should you recommend?

Accepted Answer

Train a Custom Vision classification model and deploy it to the edge on CCTV cameras. To detect objects in real-time from live CCTV feeds, the model must run at the edge with low latency. Custom Vision supports object detection models that can be exported to run on edge devices such as cameras or IoT devices. This enables real-time inference without sending video to the cloud. Azure AI Video Indexer is designed for post-processing indexed videos, not live real-time detection. Azure AI Vision Image Analysis works on single images, not live video streams.

Answer

Use Azure AI Vision Image Analysis with a custom model to analyze each frame offline

Answer

Use Azure AI Video Indexer with a custom object detection model to detect vests in video frames

Answer

Use Azure Video Analyzer for Media to index videos and detect vests after recording

Question 9

A museum wants to create an interactive exhibit where visitors take a photo of a painting and the app provides information about the artist and historical period. The solution must use a pre-built AI service and be deployed on Azure. Which approach should you recommend?

Accepted Answer

Use Azure AI Vision Image Analysis to generate captions for the paintings. Azure AI Vision Image Analysis is a pre-built AI service that can analyze images and return tags, captions, and descriptions. For famous paintings, it may recognize the artist and period from pre-trained models. Option B requires custom training, which is not pre-built. Option C is for video, and D for documents. Therefore, A is the recommended approach.

Answer

Train a Custom Vision classifier with images of paintings labeled by artist and period

Answer

Use Azure AI Video Indexer to analyze the painting image

Answer

Use Azure AI Document Intelligence to extract text from the painting

Question 10

You are building a solution that uses the Azure AI Vision Image Analysis API to extract text from scanned documents. The documents contain both printed and handwritten text in English and French. You need to configure the API call to ensure it returns the detected text and also indicates the language of the text. Which parameter should you include in your request?

Accepted Answer

Set the 'features' parameter to 'read' and set the 'language' query parameter to 'auto'.. The Azure AI Vision Image Analysis API allows you to specify the 'language' query parameter with a value of 'auto' to enable automatic language detection for the Read OCR feature. This ensures that the service identifies the language of the extracted text, which is necessary when documents contain multiple languages.

Answer

Set the 'features' parameter to 'read' and include 'language' in the request body.

Answer

Set the 'features' parameter to 'read' and set the 'language' query parameter to 'en,fr'.

Answer

Set the 'features' parameter to 'ocr' and include 'detectLanguage' in the request body.

Question 11

You are building a solution that uses Azure AI Vision to analyze images uploaded by users. You need to extract text from the images, detect objects, and generate descriptive captions. You want to use the Image Analysis API. Which two capabilities must you enable in the API call? (Choose two.)

Accepted Answer

Caption. To extract text and generate captions using the Image Analysis API, you must enable the Read and Caption visual features. Read returns recognized text, while Caption produces a descriptive sentence. Object detection is handled by a separate feature (ObjectDetection), but that is not among the options. Tags and Faces do not meet the stated requirements.

Answer

Faces

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Objects

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